import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   ShapEImgaImgPipeline
		lowerCAmelCase_     =   ['image']
		lowerCAmelCase_     =   ['image']
		lowerCAmelCase_     =   [
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		lowerCAmelCase_     =   False
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				return 3_2
		@property
		def   _snake_case       (       self     :     int		)      ->	Dict:
				return 3_2
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				return self.time_input_dim * 4
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Optional[Any]:
				return 8
		@property
		def   _snake_case       (       self     :     int		)      ->	Dict:
				torch.manual_seed(0		)
				_lowerCamelCase				   =  CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size     ,  image_size=6_4     ,  projection_dim=self.text_embedder_hidden_size     ,  intermediate_size=3_7     ,  num_attention_heads=4     ,  num_channels=3     ,  num_hidden_layers=5     ,  patch_size=1     ,  )

				_lowerCamelCase				   =  CLIPVisionModel(snake_case__		)
				return model
		@property
		def   _snake_case       (       self     :     Tuple		)      ->	Any:
				_lowerCamelCase				   =  CLIPImageProcessor(
				    crop_size=2_2_4     ,  do_center_crop=snake_case__     ,  do_normalize=snake_case__     ,  do_resize=snake_case__     ,  image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]     ,  image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]     ,  resample=3     ,  size=2_2_4     ,  )

				return image_processor
		@property
		def   _snake_case       (       self     :     List[str]		)      ->	Any:
				torch.manual_seed(0		)

				_lowerCamelCase				   =  {
				    'num_attention_heads': 2,
				    'attention_head_dim': 1_6,
				    'embedding_dim': self.time_input_dim,
				    'num_embeddings': 3_2,
				    'embedding_proj_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'num_layers': 1,
				    'clip_embed_dim': self.time_input_dim * 2,
				    'additional_embeddings': 0,
				    'time_embed_act_fn': 'gelu',
				    'norm_in_type': 'layer',
				    'embedding_proj_norm_type': 'layer',
				    'encoder_hid_proj_type': None,
				    'added_emb_type': None,
				}

				_lowerCamelCase				   =  PriorTransformer(**snake_case__		)
				return model
		@property
		def   _snake_case       (       self     :     List[Any]		)      ->	Optional[int]:
				torch.manual_seed(0		)

				_lowerCamelCase				   =  {
				    'param_shapes': (
				        (self.renderer_dim, 9_3),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				        (self.renderer_dim, 8),
				    ),
				    'd_latent': self.time_input_dim,
				    'd_hidden': self.renderer_dim,
				    'n_output': 1_2,
				    'background': (
				        0.1,
				        0.1,
				        0.1,
				    ),
				}
				_lowerCamelCase				   =  ShapERenderer(**snake_case__		)
				return model
		def   _snake_case       (       self     :     Any		)      ->	str:
				_lowerCamelCase				   =  self.dummy_prior
				_lowerCamelCase				   =  self.dummy_image_encoder
				_lowerCamelCase				   =  self.dummy_image_processor
				_lowerCamelCase				   =  self.dummy_renderer

				_lowerCamelCase				   =  HeunDiscreteScheduler(
				    beta_schedule='exp'     ,  num_train_timesteps=1_0_2_4     ,  prediction_type='sample'     ,  use_karras_sigmas=snake_case__     ,  clip_sample=snake_case__     ,  clip_sample_range=1.0     ,  )
				_lowerCamelCase				   =  {
				    'prior': prior,
				    'image_encoder': image_encoder,
				    'image_processor': image_processor,
				    'renderer': renderer,
				    'scheduler': scheduler,
				}

				return components
		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     str     ,  snake_case__     :     Optional[Any]=0		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  floats_tensor((1, 3, 6_4, 6_4)     ,  rng=random.Random(snake_case__		)		).to(snake_case__		)

				if str(snake_case__		).startswith('mps'		):
						_lowerCamelCase				   =  torch.manual_seed(snake_case__		)
				else:
						_lowerCamelCase				   =  torch.Generator(device=snake_case__		).manual_seed(snake_case__		)
				_lowerCamelCase				   =  {
				    'image': input_image,
				    'generator': generator,
				    'num_inference_steps': 1,
				    'frame_size': 3_2,
				    'output_type': 'np',
				}
				return inputs
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  'cpu'

				_lowerCamelCase				   =  self.get_dummy_components()

				_lowerCamelCase				   =  self.pipeline_class(**snake_case__		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)

				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  pipe(**self.get_dummy_inputs(snake_case__		)		)
				_lowerCamelCase				   =  output.images[0]
				_lowerCamelCase				   =  image[0, -3:, -3:, -1]

				assert image.shape == (2_0, 3_2, 3_2, 3)

				_lowerCamelCase				   =  np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ]		)

				assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
		def   _snake_case       (       self     :     Dict		)      ->	Optional[Any]:
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]		)
		def   _snake_case       (       self     :     Optional[Any]		)      ->	Tuple:
				_lowerCamelCase				   =  torch_device == 'cpu'
				_lowerCamelCase				   =  True
				self._test_inference_batch_single_identical(
				    batch_size=2     ,  test_max_difference=snake_case__     ,  relax_max_difference=snake_case__     ,  )



		def   _snake_case       (       self     :     Any		)      ->	Tuple:
				_lowerCamelCase				   =  self.get_dummy_components()
				_lowerCamelCase				   =  self.pipeline_class(**snake_case__		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)
				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  1
				_lowerCamelCase				   =  2

				_lowerCamelCase				   =  self.get_dummy_inputs(snake_case__		)

				for key in inputs.keys():
						if key in self.batch_params:
								_lowerCamelCase				   =  batch_size * [inputs[key]]

				_lowerCamelCase				   =  pipe(**snake_case__     ,  num_images_per_prompt=snake_case__		)[0]

				assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Optional[int]:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def   _snake_case       (       self     :     List[str]		)      ->	List[Any]:
				_lowerCamelCase				   =  load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'		)
				_lowerCamelCase				   =  load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_img2img_out.npy'		)
				_lowerCamelCase				   =  ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'		)
				_lowerCamelCase				   =  pipe.to(snake_case__		)
				pipe.set_progress_bar_config(disable=snake_case__		)

				_lowerCamelCase				   =  torch.Generator(device=snake_case__		).manual_seed(0		)

				_lowerCamelCase				   =  pipe(
				    snake_case__     ,  generator=snake_case__     ,  guidance_scale=3.0     ,  num_inference_steps=6_4     ,  frame_size=6_4     ,  output_type='np'     ,  ).images[0]

				assert images.shape == (2_0, 6_4, 6_4, 3)

				assert_mean_pixel_difference(snake_case__     ,  snake_case__		)
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings





@maybe_allow_in_graph
class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     Tuple=0.0     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     str = "geglu"     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = True     ,  snake_case__     :     str = "layer_norm"     ,  snake_case__     :     bool = False     ,  )      ->	Dict:
				super().__init__()
				_lowerCamelCase				   =  only_cross_attention

				_lowerCamelCase				   =  (num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
				_lowerCamelCase				   =  (num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

				if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
						raise ValueError(
						    f"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
						    f""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}."""		)

				# Define 3 blocks. Each block has its own normalization layer.
				# 1. Self-Attn
				if self.use_ada_layer_norm:
						_lowerCamelCase				   =  AdaLayerNorm(snake_case__     ,  snake_case__		)
				elif self.use_ada_layer_norm_zero:
						_lowerCamelCase				   =  AdaLayerNormZero(snake_case__     ,  snake_case__		)
				else:
						_lowerCamelCase				   =  nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__		)
				_lowerCamelCase				   =  Attention(
				    query_dim=snake_case__     ,  heads=snake_case__     ,  dim_head=snake_case__     ,  dropout=snake_case__     ,  bias=snake_case__     ,  cross_attention_dim=cross_attention_dim if only_cross_attention else None     ,  upcast_attention=snake_case__     ,  )

				# 2. Cross-Attn
				if cross_attention_dim is not None or double_self_attention:
						# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
						# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
						# the second cross attention block.
						_lowerCamelCase				   =  (
						    AdaLayerNorm(snake_case__     ,  snake_case__		)
						    if self.use_ada_layer_norm
						    else nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__		)
						)
						_lowerCamelCase				   =  Attention(
						    query_dim=snake_case__     ,  cross_attention_dim=cross_attention_dim if not double_self_attention else None     ,  heads=snake_case__     ,  dim_head=snake_case__     ,  dropout=snake_case__     ,  bias=snake_case__     ,  upcast_attention=snake_case__     ,  )  # is self-attn if encoder_hidden_states is none
				else:
						_lowerCamelCase				   =  None
						_lowerCamelCase				   =  None

				# 3. Feed-forward
				_lowerCamelCase				   =  nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__		)
				_lowerCamelCase				   =  FeedForward(snake_case__     ,  dropout=snake_case__     ,  activation_fn=snake_case__     ,  final_dropout=snake_case__		)

				# let chunk size default to None
				_lowerCamelCase				   =  None
				_lowerCamelCase				   =  0
		def   _snake_case       (       self     :     str     ,  snake_case__     :     Optional[int]     ,  snake_case__     :     int		)      ->	Optional[Any]:
				# Sets chunk feed-forward
				_lowerCamelCase				   =  chunk_size
				_lowerCamelCase				   =  dim



		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     torch.FloatTensor     ,  snake_case__     :     Optional[torch.FloatTensor] = None     ,  snake_case__     :     Optional[torch.FloatTensor] = None     ,  snake_case__     :     Optional[torch.FloatTensor] = None     ,  snake_case__     :     Optional[torch.LongTensor] = None     ,  snake_case__     :     Dict[str, Any] = None     ,  snake_case__     :     Optional[torch.LongTensor] = None     ,  )      ->	List[Any]:
				# Notice that normalization is always applied before the real computation in the following blocks.
				# 1. Self-Attention
				if self.use_ada_layer_norm:
						_lowerCamelCase				   =  self.norma(snake_case__     ,  snake_case__		)
				elif self.use_ada_layer_norm_zero:
						_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  self.norma(
						    snake_case__     ,  snake_case__     ,  snake_case__     ,  hidden_dtype=hidden_states.dtype		)
				else:
						_lowerCamelCase				   =  self.norma(snake_case__		)

				_lowerCamelCase				   =  cross_attention_kwargs if cross_attention_kwargs is not None else {}

				_lowerCamelCase				   =  self.attna(
				    snake_case__     ,  encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None     ,  attention_mask=snake_case__     ,  **snake_case__     ,  )
				if self.use_ada_layer_norm_zero:
						_lowerCamelCase				   =  gate_msa.unsqueeze(1		) * attn_output
				_lowerCamelCase				   =  attn_output + hidden_states

				# 2. Cross-Attention
				if self.attna is not None:
						_lowerCamelCase				   =  (
						    self.norma(snake_case__     ,  snake_case__		) if self.use_ada_layer_norm else self.norma(snake_case__		)
						)

						_lowerCamelCase				   =  self.attna(
						    snake_case__     ,  encoder_hidden_states=snake_case__     ,  attention_mask=snake_case__     ,  **snake_case__     ,  )
						_lowerCamelCase				   =  attn_output + hidden_states

				# 3. Feed-forward
				_lowerCamelCase				   =  self.norma(snake_case__		)

				if self.use_ada_layer_norm_zero:
						_lowerCamelCase				   =  norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

				if self._chunk_size is not None:
						# "feed_forward_chunk_size" can be used to save memory
						if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
								raise ValueError(
								    f"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."""		)

						_lowerCamelCase				   =  norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
						_lowerCamelCase				   =  torch.cat(
						    [self.ff(snake_case__		) for hid_slice in norm_hidden_states.chunk(snake_case__     ,  dim=self._chunk_dim		)]     ,  dim=self._chunk_dim     ,  )
				else:
						_lowerCamelCase				   =  self.ff(snake_case__		)

				if self.use_ada_layer_norm_zero:
						_lowerCamelCase				   =  gate_mlp.unsqueeze(1		) * ff_output

				_lowerCamelCase				   =  ff_output + hidden_states

				return hidden_states





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     int     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     int = 4     ,  snake_case__     :     float = 0.0     ,  snake_case__     :     str = "geglu"     ,  snake_case__     :     bool = False     ,  )      ->	Optional[int]:
				super().__init__()
				_lowerCamelCase				   =  int(dim * mult		)
				_lowerCamelCase				   =  dim_out if dim_out is not None else dim

				if activation_fn == "gelu":
						_lowerCamelCase				   =  GELU(snake_case__     ,  snake_case__		)
				if activation_fn == "gelu-approximate":
						_lowerCamelCase				   =  GELU(snake_case__     ,  snake_case__     ,  approximate='tanh'		)
				elif activation_fn == "geglu":
						_lowerCamelCase				   =  GEGLU(snake_case__     ,  snake_case__		)
				elif activation_fn == "geglu-approximate":
						_lowerCamelCase				   =  ApproximateGELU(snake_case__     ,  snake_case__		)

				_lowerCamelCase				   =  nn.ModuleList([]		)
				# project in
				self.net.append(snake_case__		)
				# project dropout
				self.net.append(nn.Dropout(snake_case__		)		)
				# project out
				self.net.append(nn.Linear(snake_case__     ,  snake_case__		)		)
				# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
				if final_dropout:
						self.net.append(nn.Dropout(snake_case__		)		)



		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     List[Any]		)      ->	Optional[int]:
				for module in self.net:
						_lowerCamelCase				   =  module(snake_case__		)
				return hidden_states





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     str     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     str = "none"		)      ->	Any:
				super().__init__()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  snake_case__		)
				_lowerCamelCase				   =  approximate
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     List[Any]		)      ->	int:
				if gate.device.type != "mps":
						return F.gelu(snake_case__     ,  approximate=self.approximate		)
				# mps: gelu is not implemented for float16
				return F.gelu(gate.to(dtype=torch.floataa		)     ,  approximate=self.approximate		).to(dtype=gate.dtype		)



		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     Union[str, Any]		)      ->	str:
				_lowerCamelCase				   =  self.proj(snake_case__		)
				_lowerCamelCase				   =  self.gelu(snake_case__		)
				return hidden_states





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	List[Any]:
				super().__init__()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  dim_out * 2		)
		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Optional[int]		)      ->	Optional[Any]:
				if gate.device.type != "mps":
						return F.gelu(snake_case__		)
				# mps: gelu is not implemented for float16
				return F.gelu(gate.to(dtype=torch.floataa		)		).to(dtype=gate.dtype		)



		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     Optional[Any]		)      ->	Optional[int]:
				_lowerCamelCase   ,							_lowerCamelCase				   =  self.proj(snake_case__		).chunk(2     ,  dim=-1		)
				return hidden_states * self.gelu(snake_case__		)





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Tuple:
				super().__init__()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  snake_case__		)



		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     List[Any]		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.proj(snake_case__		)
				return x * torch.sigmoid(1.702 * x		)





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Optional[Any]     ,  snake_case__     :     Tuple     ,  snake_case__     :     Any		)      ->	Dict:
				super().__init__()
				_lowerCamelCase				   =  nn.Embedding(snake_case__     ,  snake_case__		)
				_lowerCamelCase				   =  nn.SiLU()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  embedding_dim * 2		)
				_lowerCamelCase				   =  nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__		)



		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     str     ,  snake_case__     :     Dict		)      ->	Optional[int]:
				_lowerCamelCase				   =  self.linear(self.silu(self.emb(snake_case__		)		)		)
				_lowerCamelCase   ,							_lowerCamelCase				   =  torch.chunk(snake_case__     ,  2		)
				_lowerCamelCase				   =  self.norm(snake_case__		) * (1 + scale) + shift
				return x





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Any     ,  snake_case__     :     Optional[Any]     ,  snake_case__     :     Union[str, Any]		)      ->	Dict:
				super().__init__()

				_lowerCamelCase				   =  CombinedTimestepLabelEmbeddings(snake_case__     ,  snake_case__		)

				_lowerCamelCase				   =  nn.SiLU()
				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  6 * embedding_dim     ,  bias=snake_case__		)
				_lowerCamelCase				   =  nn.LayerNorm(snake_case__     ,  elementwise_affine=snake_case__     ,  eps=1e-6		)



		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     Tuple     ,  snake_case__     :     str     ,  snake_case__     :     str     ,  snake_case__     :     List[Any]=None		)      ->	int:
				_lowerCamelCase				   =  self.linear(self.silu(self.emb(snake_case__     ,  snake_case__     ,  hidden_dtype=snake_case__		)		)		)
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  emb.chunk(6     ,  dim=1		)
				_lowerCamelCase				   =  self.norm(snake_case__		) * (1 + scale_msa[:, None]) + shift_msa[:, None]
				return x, gate_msa, shift_mlp, scale_mlp, gate_mlp





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Tuple     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     int     ,  snake_case__     :     Optional[str] = None     ,  snake_case__     :     float = 1e-5		)      ->	int:
				super().__init__()
				_lowerCamelCase				   =  num_groups
				_lowerCamelCase				   =  eps

				if act_fn is None:
						_lowerCamelCase				   =  None
				else:
						_lowerCamelCase				   =  get_activation(snake_case__		)

				_lowerCamelCase				   =  nn.Linear(snake_case__     ,  out_dim * 2		)



		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     int     ,  snake_case__     :     Optional[Any]		)      ->	Union[str, Any]:
				if self.act:
						_lowerCamelCase				   =  self.act(snake_case__		)
				_lowerCamelCase				   =  self.linear(snake_case__		)
				_lowerCamelCase				   =  emb[:, :, None, None]
				_lowerCamelCase   ,							_lowerCamelCase				   =  emb.chunk(2     ,  dim=1		)

				_lowerCamelCase				   =  F.group_norm(snake_case__     ,  self.num_groups     ,  eps=self.eps		)
				_lowerCamelCase				   =  x * (1 + scale) + shift
				return x